Contextual Out-of-Domain Utterance Handling With Counterfeit Data Augmentation

Neural dialog models often lack robustness to anomalous user input and produce inappropriate responses which leads to frustrating user experience. Although there are a set of prior approaches to out-of-domain (OOD) utterance detection, they share a few restrictions: they rely on OOD data or multiple sub-domains, and their OOD detection is context-independent which leads to suboptimal performance in a dialog. The goal of this paper is to propose a novel OOD detection method that does not require OOD data by utilizing counterfeit OOD turns in the context of a dialog. For the sake of fostering further research, we also release new dialog datasets which are 3 publicly available dialog corpora augmented with OOD turns in a controllable way. Our method outperforms state-of-the-art dialog models equipped with a conventional OOD detection mechanism by a large margin in the presence of OOD utterances.Comment: ICASSP 201

Weakly and Strongly Constrained Dialogues for Language Learning

International audienceWe present two dialogue systems for lan- guage learning which both restrict the di- alog to a specific domain thereby pro- moting robustness and the learning of a given vocabulary. The systems vary in how much they constrain the learner's answer : one system places no other constrain on the learner than that provided by the re- stricted domain and the dialog context ; the other provides the learner with an exercise whose solution is the expected answer. The first system uses supervised learning for simulating a human tutor whilst the second one uses natural language gener- ation techniques to produce grammar ex- ercises which guide the learner toward the expected answer